Оффлайн
- Регистрация
- 1 Мар 2015
- Сообщения
- 1,481
- Баллы
- 155
Take this as GIFT :
I was tired of half-finishing tutorials.
I was tired of seeing job listings with words I didn’t know.
I knew just enough Python to get by, but not enough to ship.
And then, almost by accident, I discovered a new approach:
I stopped trying to learn Python the “right” way. I started building things with AI instead.
Let me walk you through how I did it—and how you can do the same starting today.
? Why This Works (Even If You're Not “Good” at Code)
Most beginner devs fall into two traps:
With GPT and Python, you can skip both traps. You get a feedback loop:
?️ Project 1: A Daily Journal That Stores Entries as JSON
I started simple.
Prompt:
import json, sys, os
from datetime import datetime
FILE = "journal.json"
entries = []
if os.path.exists(FILE):
with open(FILE, "r") as f:
entries = json.load(f)
entry = input("Write your journal entry:\n> ")
timestamp = datetime.now().isoformat()
entries.append({"timestamp": timestamp, "entry": entry})
with open(FILE, "w") as f:
json.dump(entries, f, indent=2)
print("Saved.")
What I Learned:
More importantly, I had something useful I could run every day.
? Project 2: A Game That Plays Rock Paper Scissors
This one was purely for fun.
Prompt:
import random
options = ["rock", "paper", "scissors"]
user = input("Choose rock, paper, or scissors: ").lower()
comp = random.choice(options)
print(f"Computer chose: {comp}")
if user == comp:
print("It's a tie!")
elif (user == "rock" and comp == "scissors") or \
(user == "paper" and comp == "rock") or \
(user == "scissors" and comp == "paper"):
print("You win!")
else:
print("You lose!")
What I Learned:
I wanted to organize my project ideas by date.
Prompt:
import csv
from datetime import datetime
FILENAME = "ideas.csv"
idea = input("What's your new idea?\n> ")
date = datetime.now().strftime("%Y-%m-%d")
with open(FILENAME, "a", newline="") as f:
writer = csv.writer(f)
writer.writerow([date, idea])
print("\nCurrent Ideas:\n")
with open(FILENAME) as f:
reader = csv.reader(f)
sorted_ideas = sorted(reader, key=lambda x: x[0], reverse=True)
for row in sorted_ideas:
print(f"{row[0]} - {row[1]}")
What I Learned:
Every script gave me bite-sized Python lessons without needing to study.
Instead of theory, I got:
I even started getting curious:
? Bonus: Let GPT Fix Its Own Mistakes
Here’s where it gets wild.
Sometimes the AI writes broken code. But if you copy the error and paste it back into the chat?
It learns. And it fixes it.
Example:
? Want 100+ AI-Powered Python Project Ideas?
? Final Thoughts: Build First, Understand Later
If you're stuck in tutorial hell, here’s what you need to remember:
Describe what you want.
Run the code.
Break it.
Fix it.
Each time you do, you’ll get smarter—without even realizing it.
By the time you do revisit those Python textbooks or take that next course, everything will click.
So go ahead:
Open a terminal.
Open ChatGPT.
And build your first AI-assisted Python script today.
? 10 Awesome Courses You’ll Actually Enjoy
If you're the kind of person who likes peeling back the layers of computing, tech history, and programming weirdness—these curated learning kits will absolutely fuel your curiosity.
Each is a self-contained, text-based course you can read, study, or even remix into your own learning journey:
? Each one is packed with insights, stories, and lessons—great for developers, tech writers, and anyone fascinated by the hidden history and culture of tech.
? Featured Learning Kit
Here’s one course you shouldn’t miss:
You know Windows. You know Linux. Now meet the OSes they don’t want you to touch.Most people only ever interact with a sanitized surface. But behind the curtain lives a wild ecosystem of forgotten, forbidden, and experimental operating systems—built by rebels, tinkerers, and visionaries.Explore: Mythical and misunderstood: TempleOS, SerenityOS, Plan 9, BeOS, AmigaOS, SkyOS. Security-first outliers: Qubes OS, Genode, Redox, SeL4, Singularity, Barrelfish. Retro-futurist visions: NeXTSTEP, MINIX, Haiku, DR-DOS, CP/M, GEOS, MorphOS. Research lab relics: Multics, TENEX, Mach, Fuchsia, Exokernel, Amoeba, Spring. Stripped-down madness: MenuetOS, KolibriOS, Inferno, Phantom OS, A2, ToaruOS. Embedded + real-time beasts: FreeRTOS, ChibiOS, Contiki, TinyOS, HelenOS. These aren’t just alt-OSes. They’re alt-realities.If you’ve ever wondered what computing could look like if it weren’t monopolized—this is your underground tour.
snappytuts.gumroad.com
- And this :
Just it, Enjoy the below article....
That was the question I asked myself when I hit a wall.
I was tired of half-finishing tutorials.
I was tired of seeing job listings with words I didn’t know.
I knew just enough Python to get by, but not enough to ship.
And then, almost by accident, I discovered a new approach:
I stopped trying to learn Python the “right” way. I started building things with AI instead.
Let me walk you through how I did it—and how you can do the same starting today.
? Why This Works (Even If You're Not “Good” at Code)
Most beginner devs fall into two traps:
- Trap 1: They read too much and build too little.
- Trap 2: They build the wrong stuff—too boring or too complicated.
With GPT and Python, you can skip both traps. You get a feedback loop:
And best of all? You don’t need to “master” anything first. You start with curiosity and finish with a working project.You describe what you want
? AI writes it
? You run it, find bugs, and ask for fixes
? You learn while shipping real code
?️ Project 1: A Daily Journal That Stores Entries as JSON
I started simple.
Prompt:
Output (trimmed):
import json, sys, os
from datetime import datetime
FILE = "journal.json"
entries = []
if os.path.exists(FILE):
with open(FILE, "r") as f:
entries = json.load(f)
entry = input("Write your journal entry:\n> ")
timestamp = datetime.now().isoformat()
entries.append({"timestamp": timestamp, "entry": entry})
with open(FILE, "w") as f:
json.dump(entries, f, indent=2)
print("Saved.")
What I Learned:
- How json.load() and json.dump() work
- How to handle file not found errors
- How to structure basic CLI input/output
More importantly, I had something useful I could run every day.
? Project 2: A Game That Plays Rock Paper Scissors
This one was purely for fun.
Prompt:
GPT nailed it:
import random
options = ["rock", "paper", "scissors"]
user = input("Choose rock, paper, or scissors: ").lower()
comp = random.choice(options)
print(f"Computer chose: {comp}")
if user == comp:
print("It's a tie!")
elif (user == "rock" and comp == "scissors") or \
(user == "paper" and comp == "rock") or \
(user == "scissors" and comp == "paper"):
print("You win!")
else:
print("You lose!")
What I Learned:
- random.choice() and basic logic flow
- Clean way to write conditionals
- How to turn beginner exercises into CLI games
I wanted to organize my project ideas by date.
Prompt:
GPT built this using csv and datetime.
import csv
from datetime import datetime
FILENAME = "ideas.csv"
idea = input("What's your new idea?\n> ")
date = datetime.now().strftime("%Y-%m-%d")
with open(FILENAME, "a", newline="") as f:
writer = csv.writer(f)
writer.writerow([date, idea])
print("\nCurrent Ideas:\n")
with open(FILENAME) as f:
reader = csv.reader(f)
sorted_ideas = sorted(reader, key=lambda x: x[0], reverse=True)
for row in sorted_ideas:
print(f"{row[0]} - {row[1]}")
What I Learned:
- How to use the csv module
- How to append without overwriting
- How to sort records by date (without pandas!)
Every script gave me bite-sized Python lessons without needing to study.
Instead of theory, I got:
- Instant syntax examples
- Clear structure (what goes where)
- Fixes for every error I hit
I even started getting curious:
That’s the magic. Curiosity drives real learning—not just watching someone else’s YouTube tutorial.
? Bonus: Let GPT Fix Its Own Mistakes
Here’s where it gets wild.
Sometimes the AI writes broken code. But if you copy the error and paste it back into the chat?
It learns. And it fixes it.
Example:
Just like a mentor, GPT guides you through what went wrong, not just how to fix it.
? Want 100+ AI-Powered Python Project Ideas?
Bookmark it here:
A curated site where beginner and intermediate developers can:
- Explore handpicked tools
- Read fresh Python articles
- Get GitHub & StackOverflow trends
Dive in:
- ?
- ?
- ?
? Final Thoughts: Build First, Understand Later
If you're stuck in tutorial hell, here’s what you need to remember:
Start with AI.
Describe what you want.
Run the code.
Break it.
Fix it.
Each time you do, you’ll get smarter—without even realizing it.
By the time you do revisit those Python textbooks or take that next course, everything will click.
So go ahead:
Open a terminal.
Open ChatGPT.
And build your first AI-assisted Python script today.
? 10 Awesome Courses You’ll Actually Enjoy
If you're the kind of person who likes peeling back the layers of computing, tech history, and programming weirdness—these curated learning kits will absolutely fuel your curiosity.
Each is a self-contained, text-based course you can read, study, or even remix into your own learning journey:
- ?
- ?️
- ?
- ?️
- ?
- ?
- ?
- ?
- ?
? Each one is packed with insights, stories, and lessons—great for developers, tech writers, and anyone fascinated by the hidden history and culture of tech.
? Featured Learning Kit
Here’s one course you shouldn’t miss:
You know Windows. You know Linux. Now meet the OSes they don’t want you to touch.Most people only ever interact with a sanitized surface. But behind the curtain lives a wild ecosystem of forgotten, forbidden, and experimental operating systems—built by rebels, tinkerers, and visionaries.Explore: Mythical and misunderstood: TempleOS, SerenityOS, Plan 9, BeOS, AmigaOS, SkyOS. Security-first outliers: Qubes OS, Genode, Redox, SeL4, Singularity, Barrelfish. Retro-futurist visions: NeXTSTEP, MINIX, Haiku, DR-DOS, CP/M, GEOS, MorphOS. Research lab relics: Multics, TENEX, Mach, Fuchsia, Exokernel, Amoeba, Spring. Stripped-down madness: MenuetOS, KolibriOS, Inferno, Phantom OS, A2, ToaruOS. Embedded + real-time beasts: FreeRTOS, ChibiOS, Contiki, TinyOS, HelenOS. These aren’t just alt-OSes. They’re alt-realities.If you’ve ever wondered what computing could look like if it weren’t monopolized—this is your underground tour.